Mitochondria and chloroplasts were originally independent organisms, which were engulfed and harnessed by an ancestral cell in the distant evolutionary past. These organelles retain their own genomes, and continue to replicate, degrade, mutate, and recombine in modern-day plant cells. These rich dynamics make these cellular power plants a fascinating “evolutionary system within cells” – and one that is of fundamental importance in maintaining the energy supplies that plants need to survive and crops need to grow.

Despite the fact that plant organelles feed the world, through photosynthesis and respiration, much remains to be understood about how these populations evolve within cells. This project will use an exciting and highly transferrable combination of tools from machine learning, computer simulation, and synthetic biology to make progress describing and exploring these rich, vital systems.

Throughout evolutionary history, mitochondria and chloroplasts have lost many of their genes to the host cell nucleus. Why they retain the genes they do is currently poorly understood, and of vital importance in understanding how complex life has evolved, and how plants produce energy and food. How the cell maintains the these populations of organellar DNA – power station blueprints – is also of profound importance in understanding crop diseases and stress responses.

Recent work from our group has combined evolutionary modelling with tools from data science to provide answers to some of these questions in mitochondria – identifying the features that dictate whether genes will be retained in mtDNA through evolution [1, 2], and explaining how mtDNA populations avoid the buildup of mutations over time [3]. The parallels to these questions, and many others, in chloroplasts remain unanswered. Progress here will both help elucidate “universal” rules underlying organelle evolution over the billions of years of eukaryotic history, and to understand how plants (and humans) can control their vital energetic organelles.

We are now in the exciting position of being able to simultaneously probe these questions experimentally and theoretically, using a combination of tools from synthetic biology and computational modelling. In green algae (a useful model system), organelle DNA can be transformed using UoB’s biolistic (“gene gun”) facilities, allowing us to artificially engineer the genetic content of organelles. The results of genetic perturbation on organismal fitness and performance under fluctuating environments can be assayed using our lab’s bioreactor, which cultures algae under completely controllable dynamic temperature and light schemes. We can also use machine learning and bioinformatic tools (full training will be provided) to computationally explore the evolutionary history of bioenergetic organelles, developing theory and synthetic biology to probe the artificial engineering of these evolved systems.

This project will combine these approaches, using computational modelling to identify synthetic perturbations that may modulate organelle and photosynthetic performance, and using synthetic biology with biolistics and bioreactor assays in algae to test these predictions. The researcher will harness the power of interdisciplinary approaches in biosciences both to unpick evolutionary history and help learn how we can optimise the control of photosynthesis through synthetic approaches.

This project would suit candidates interested in the intersection between “blue skies” evolutionary questions and questions of translatable importance. Some experience of biolistics, and some mathematical and/or statistical background would be an advantage, and an interest in developing mathematical and data science skills is essential.